Data Engineer

• Define the overall data platform architecture (Lakehouse/EDW), including reference patterns (Medallion, Lambda, Kappa), technology selection, and integration blueprint.
• Design conceptual, logical, and physical data models to support multi-tenant and vertical-specific data products; standardize logical layers (ingest/raw, staged/curated, serving).
• Establish data governance, metadata, cataloging (e.g., Unity Catalog), lineage, data contracts, and classification practices to support analytics and ML use cases.
• Define security and compliance controls: access management (RBAC/IAM), data masking, encryption (in transit/at rest), network segmentation, and audit policies.
• Architect scalability, high availability, disaster recovery (RPO/RTO), and capacity & cost management strategies for cloud and hybrid deployments.
• Lead selection and integration of platform components (Databricks, Delta Lake, Delta Live Tables, Fivetran, Azure Data Factory / Data Fabric, orchestration, monitoring/observability).
• Design and enforce CI/CD patterns for data artifacts (notebooks, packages, infra-as-code), including testing, automated deployments and rollback strategies.
• Define ingestion patterns (batch & streaming), file compacting/compaction strategies, partitioning schemes, and storage layout to optimize IO and costs.
• Specify observability practices: metrics, SLAs, health dashboards, structured logging, tracing, and alerting for pipelines and jobs.
• Act as technical authority and mentor for Data Engineering teams; perform architecture and code reviews for critical components.
• Collaborate with stakeholders (Data Product Owners, Security, Infrastructure, BI, ML) to translate business requirements into technical solutions and roadmap.
• Design, develop, test, and deploy processing modules using Spark (PySpark/Scala), Spark SQL, and database stored procedures where applicable.
• Build and optimize data pipelines on Databricks and complementary engines (SQL Server, Azure SQL, AWS RDS/Aurora, PostgreSQL, Oracle).
• Implement DevOps practices: infra-as-code, CI/CD pipelines (ingestion, transformation, tests, deployment), automated testing and version control.
• Troubleshoot and resolve complex data quality, performance, and availability issues; recommend and implement continuous improvements.

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